After years of facing flak from global health experts for not doing enough to tackle worsening air pollution, India now says it will launch its own national air quality index in the next five years.
The index will rank 66 Indian cities and provide associated health risks in a color-coded manner that can be understood by everyone. The move, officials say, could raise public awareness of an issue that many Indians often overlook, but also push them to demand higher quality standards and laws.
“The index is meant to alert us, but it has to also drive us to action,” said Ashok Lavasa, secretary of the ministry of environment, forest and climate change, at an event in New Delhi on Friday to announce the launch.
Earlier this year, a report by a Yale Univrsity research team showed that India ranked 174th of 178 countries in air quality, somewhere close to China and Pakistan.
Then came the World Health Organization, that said New Delhi’s air quality is the worst in the world, and that its annual average concentration of small particles was almost three times that of Beijing.
The reports prompted environmentalists here to call for stricter fuel emission standards across India.
Officials here had at that time dismissed it as “biased.”
On Friday, India touted the launch of its own index.
“There was a lot of hue and cry over claims made by some foreign people at that time that the air quality in Delhi was the worst,” said Susheel Kumar, chairman of the Central Pollution Control Board. “We want to come out with our own national air quality index. Our team of experts are second to none in the world.”
Air quality has steadily worsened in the past two decades in India, caused largely by rapid industrial growth, use of coal and growing urban traffic. Experts say that air pollution is the fifth-largest killer in India.
But producing credible pollution data is easier said than done.
A newspaper article this week titled, “Why India’s Numbers on Air Quality Can't be Trusted,” said state pollution control boards routinely under-report data for fear of looking bad; use dodgy machines that generate unreliable data; and routinely use imported machines that do not work well in local conditions.